Abstract Objectives The provisional criteria of the American College of Rheumatology (ACR) 2010 and the 2011 self-report modification for survey and clinical research are widely used for fibromyalgia ...diagnosis. To determine the validity, usefulness, potential problems, and modifications required for the criteria, we assessed multiple research reports published in 2010–2016 in order to provide a 2016 update to the criteria. Methods We reviewed 14 validation studies that compared 2010/2011 criteria with ACR 1990 classification and clinical criteria, as well as epidemiology, clinical, and databank studies that addressed important criteria-level variables. Based on definitional differences between 1990 and 2010/2011 criteria, we interpreted 85% sensitivity and 90% specificity as excellent agreement. Results Against 1990 and clinical criteria, the median sensitivity and specificity of the 2010/2011 criteria were 86% and 90%, respectively. The 2010/2011 criteria led to misclassification when applied to regional pain syndromes, but when a modified widespread pain criterion (the “generalized pain criterion”) was added misclassification was eliminated. Based on the above data and clinic usage data, we developed a (2016) revision to the 2010/2011 fibromyalgia criteria. Fibromyalgia may now be diagnosed in adults when all of the following criteria are met: (1) Generalized pain, defined as pain in at least 4 of 5 regions, is present. (2) Symptoms have been present at a similar level for at least 3 months. (3) Widespread pain index (WPI) ≥ 7 and symptom severity scale (SSS) score ≥ 5 OR WPI of 4–6 and SSS score ≥ 9. (4) A diagnosis of fibromyalgia is valid irrespective of other diagnoses. A diagnosis of fibromyalgia does not exclude the presence of other clinically important illnesses. Conclusions The fibromyalgia criteria have good sensitivity and specificity. This revision combines physician and questionnaire criteria, minimizes misclassification of regional pain disorders, and eliminates the previously confusing recommendation regarding diagnostic exclusions. The physician-based criteria are valid for individual patient diagnosis. The self-report version of the criteria is not valid for clinical diagnosis in individual patients but is valid for research studies. These changes allow the criteria to function as diagnostic criteria, while still being useful for classification.
Luminescence thermal sensing and deep-tissue imaging using nanomaterials operating within the first biological window (ca. 700-980 nm) are of great interest, prompted by the ever-growing demands in ...the fields of nanotechnology and nanomedicine. Here, we show that (Gd1-xNdx)2O3 (x = 0.009, 0.024 and 0.049) nanorods exhibit one of the highest thermal sensitivity and temperature uncertainty reported so far (1.75 ± 0.04% K(-1) and 0.14 ± 0.05 K, respectively) for a nanothermometer operating in the first transparent near infrared window at temperatures in the physiological range. This sensitivity value is achieved using a common R928 photomultiplier tube that allows defining the thermometric parameter as the integrated intensity ratio between the (4)F5/2 → (4)I9/2 and (4)F3/2 → (4)I9/2 transitions (with an energy difference between the barycentres of the two transitions >1000 cm(-1)). Moreover, the measured sensitivity is one order of magnitude higher than the values reported so far for Nd(3+)-based nanothermometers enlarging, therefore, the potential of using Nd(3+) ions in luminescence thermal sensing and deep-tissue imaging.
Abstract
The virulence factor database (VFDB, http://www.mgc.ac.cn/VFs/) is devoted to providing the scientific community with a comprehensive warehouse and online platform for deciphering bacterial ...pathogenesis. The various combinations, organizations and expressions of virulence factors (VFs) are responsible for the diverse clinical symptoms of pathogen infections. Currently, whole-genome sequencing is widely used to decode potential novel or variant pathogens both in emergent outbreaks and in routine clinical practice. However, the efficient characterization of pathogenomic compositions remains a challenge for microbiologists or physicians with limited bioinformatics skills. Therefore, we introduced to VFDB an integrated and automatic pipeline, VFanalyzer, to systematically identify known/potential VFs in complete/draft bacterial genomes. VFanalyzer first constructs orthologous groups within the query genome and preanalyzed reference genomes from VFDB to avoid potential false positives due to paralogs. Then, it conducts iterative and exhaustive sequence similarity searches among the hierarchical prebuilt datasets of VFDB to accurately identify potential untypical/strain-specific VFs. Finally, via a context-based data refinement process for VFs encoded by gene clusters, VFanalyzer can achieve relatively high specificity and sensitivity without manual curation. In addition, a thoroughly optimized interactive web interface is introduced to present VFanalyzer reports in comparative pathogenomic style for easy online analysis.
•Clinical sensitivity of 44 rapid antigen tests for SARS-CoV-2 varied from 3% to 94%.•27 rapid antigen tests for SARS-CoV-2 performed significantly worse than the best one.•Analytical sensitivity ...cannot be directly translated into clinical sensitivity.
The SARS-CoV-2 pandemic has resulted in massive testing by Rapid Antigen Tests (RAT) without solid independent data regarding clinical performance being available. Thus, decision on purchase of a specific RAT may rely on manufacturer-provided data and limited peer-reviewed data.
This study consists of two parts. In the retrospective analytical part, 33 RAT from 25 manufacturers were compared to RT-PCR on 100 negative and 204 positive deep oropharyngeal cavity samples divided into four groups based on RT-PCR Cq levels. In the prospective clinical part, nearly 200 individuals positive for SARS-CoV-2 and nearly 200 individuals negative for SARS-CoV-2 by routine RT-PCR testing were retested within 72 h for each of 44 included RAT from 26 manufacturers applying RT-PCR as the reference method.
The overall analytical sensitivity differed significantly between the 33 included RAT; from 2.5% (95% CI 0.5–4.8) to 42% (95% CI 35–49). All RAT presented analytical specificities of 100%. Likewise, the overall clinical sensitivity varied significantly between the 44 included RAT; from 2.5% (95% CI 0.5–4.8) to 94% (95% CI 91–97). All RAT presented clinical specificities between 98 and 100%.
The study presents analytical as well as clinical performance data for 44 commercially available RAT compared to the same RT-PCR test. The study enables identification of individual RAT that has significantly higher sensitivity than other included RAT and may aid decision makers in selecting between the included RAT.
The study was funded by a participant fee for each test and the Danish Regions.
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Unraveling the drivers controlling community assembly is a central issue in ecology. Although it is generally accepted that selection, dispersal, diversification and drift are major community ...assembly processes, defining their relative importance is very challenging. Here, we present a framework to quantitatively infer community assembly mechanisms by phylogenetic bin-based null model analysis (iCAMP). iCAMP shows high accuracy (0.93-0.99), precision (0.80-0.94), sensitivity (0.82-0.94), and specificity (0.95-0.98) on simulated communities, which are 10-160% higher than those from the entire community-based approach. Application of iCAMP to grassland microbial communities in response to experimental warming reveals dominant roles of homogeneous selection (38%) and 'drift' (59%). Interestingly, warming decreases 'drift' over time, and enhances homogeneous selection which is primarily imposed on Bacillales. In addition, homogeneous selection has higher correlations with drought and plant productivity under warming than control. iCAMP provides an effective and robust tool to quantify microbial assembly processes, and should also be useful for plant and animal ecology.
Objective
Behçet's disease (BD) is a chronic, relapsing, inflammatory vascular disease with no pathognomonic test. Low sensitivity of the currently applied International Study Group (ISG) clinical ...diagnostic criteria led to their reassessment.
Methods
An International Team for the Revision of the International Criteria for BD (from 27 countries) submitted data from 2556 clinically diagnosed BD patients and 1163 controls with BD‐mimicking diseases or presenting at least one major BD sign. These were randomly divided into training and validation sets. Logistic regression, ‘leave‐one‐country‐out’ cross‐validation and clinical judgement were employed to develop new International Criteria for BD (ICBD) with the training data. Existing and new criteria were tested for their performance in the validation set.
Results
For the ICBD, ocular lesions, oral aphthosis and genital aphthosis are each assigned 2 points, while skin lesions, central nervous system involvement and vascular manifestations 1 point each. The pathergy test, when used, was assigned 1 point. A patient scoring ≥4 points is classified as having BD. In the training set, 93.9% sensitivity and 92.1% specificity were assessed compared with 81.2% sensitivity and 95.9% specificity for the ISG criteria. In the validation set, ICBD demonstrated an unbiased estimate of sensitivity of 94.8% (95% CI: 93.4–95.9%), considerably higher than that of the ISG criteria (85.0%). Specificity (90.5%, 95% CI: 87.9–92.8%) was lower than that of the ISG‐criteria (96.0%), yet still reasonably high. For countries with at least 90%‐of‐cases and controls having a pathergy test, adding 1 point for pathergy test increased the estimate of sensitivity from 95.5% to 98.5%, while barely reducing specificity from 92.1% to 91.6%.
Conclusion
The new proposed criteria derived from multinational data exhibits much improved sensitivity over the ISG criteria while maintaining reasonable specificity. It is proposed that the ICBD criteria to be adopted both as a guide for diagnosis and classification of BD.
Sensitivity analysis is useful in assessing how robust an association is to potential unmeasured or uncontrolled confounding. This article introduces a new measure called the "E-value," which is ...related to the evidence for causality in observational studies that are potentially subject to confounding. The E-value is defined as the minimum strength of association, on the risk ratio scale, that an unmeasured confounder would need to have with both the treatment and the outcome to fully explain away a specific treatment-outcome association, conditional on the measured covariates. A large E-value implies that considerable unmeasured confounding would be needed to explain away an effect estimate. A small E-value implies little unmeasured confounding would be needed to explain away an effect estimate. The authors propose that in all observational studies intended to produce evidence for causality, the E-value be reported or some other sensitivity analysis be used. They suggest calculating the E-value for both the observed association estimate (after adjustments for measured confounders) and the limit of the confidence interval closest to the null. If this were to become standard practice, the ability of the scientific community to assess evidence from observational studies would improve considerably, and ultimately, science would be strengthened.
Purpose To evaluate the efficacy of deep convolutional neural networks (DCNNs) for detecting tuberculosis (TB) on chest radiographs. Materials and Methods Four deidentified HIPAA-compliant datasets ...were used in this study that were exempted from review by the institutional review board, which consisted of 1007 posteroanterior chest radiographs. The datasets were split into training (68.0%), validation (17.1%), and test (14.9%). Two different DCNNs, AlexNet and GoogLeNet, were used to classify the images as having manifestations of pulmonary TB or as healthy. Both untrained and pretrained networks on ImageNet were used, and augmentation with multiple preprocessing techniques. Ensembles were performed on the best-performing algorithms. For cases where the classifiers were in disagreement, an independent board-certified cardiothoracic radiologist blindly interpreted the images to evaluate a potential radiologist-augmented workflow. Receiver operating characteristic curves and areas under the curve (AUCs) were used to assess model performance by using the DeLong method for statistical comparison of receiver operating characteristic curves. Results The best-performing classifier had an AUC of 0.99, which was an ensemble of the AlexNet and GoogLeNet DCNNs. The AUCs of the pretrained models were greater than that of the untrained models (P < .001). Augmenting the dataset further increased accuracy (P values for AlexNet and GoogLeNet were .03 and .02, respectively). The DCNNs had disagreement in 13 of the 150 test cases, which were blindly reviewed by a cardiothoracic radiologist, who correctly interpreted all 13 cases (100%). This radiologist-augmented approach resulted in a sensitivity of 97.3% and specificity 100%. Conclusion Deep learning with DCNNs can accurately classify TB at chest radiography with an AUC of 0.99. A radiologist-augmented approach for cases where there was disagreement among the classifiers further improved accuracy.
RSNA, 2017.
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